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Record W4293916807 · doi:10.54097/hset.v11i.1266

Solid Lipid Nanoparticles: A Nano Drug Carrying System in Treatment of Nervous Diseases

2022· article· en· W4293916807 on OpenAlex
Yue Yin, Jingyuan Zhang, Xinyue Zhou

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSolid lipid nanoparticleDrug deliveryBiocompatibilityDrugCentral nervous systemPharmacologyMedicineNanotechnologyChemistryMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

Solid lipid nanoparticle (SLN) is a unique colloidal system used to deliver drugs which is nontoxic, biodegradable, showing good biocompatibility, and have small particle size. The possibility of SLN to deliver the brain drugs without damaging the brain-blood barrier (BBB) makes SLN an advanced central nervous system (CNS) drug delivery system. SLNs delivering drugs to CNS are mostly prepared by applying high energy homogenization method to achieve a better surface modification. The central topic of this article is how the SLN can overcome the BBB and help treat the central neural system disease. Also, SLNs contain levodopa can go through the BBB to help treat Parkinson’s and SLNs coated with chitosan and loaded with ferric acid to treat Alzheimer’s Disease (AD) are highlighted in this article. The effectiveness of SLNs compared with traditional therapy is shown in the article. Additionally, further studies are needed to focus on higher encapsulation efficiency and drug load efficiency as well as the targeted intranasal drug delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.633

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.343
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it